Fluctuating Confidence in Stock Markets: Implications for Returns and Volatility

David, Alexander

doi:10.2307/2331232

Cited by 229 publications

(156 citation statements)

References 42 publications

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“…However, in reality, investment opportunities are partially observable as moments of probability distributions of investment opportunities are often unobservable and must be estimated from observed market signals. Dothan and Feldman (1986) and Detemple (1986) were the first to study asset prices under incomplete information in general equilibrium, followed by David (1997), Veronesi (1999), Ai (2009), among others. 1 Other papers, to name a few, Gennotte (1986), Brennan (1998), Lakner (1998) and Honda (2003), analyze dynamic portfolio choice under incomplete information.…”

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confidence: 99%

“…The importance of model uncertainty (or ambiguity) has been largely recognized in both the asset pricing literature (e.g., Anderson My aim in this paper is to examine the effects of ambiguity on intertemporal consumption and portfolio decisions in an incomplete information economy. 2 To this end, I follow Honda (2003) 1 David (1997) investigates unobservable and regime switching investment opportunities in continuous time. Lundtofte (2008) examines expected life-time utility and hedging demands when endowments and their expected growth rate are imperfectly correlated.…”

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confidence: 99%

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Dynamic portfolio choice under ambiguity and regime switching mean returns

Liu

2011

Journal of Economic Dynamics and Control

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confidence: 99%

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confidence: 99%

Dynamic portfolio choice under ambiguity and regime switching mean returns

Liu

2011

Journal of Economic Dynamics and Control

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“…Such an approach, within the long-run risks setup, is pursued in Croce et al (2010), while Ai (2010), David (1997) and Veronesi (1999) develop a model where the agents learn about the regime shifts. It is worth emphasizing that learning considered in these models does not generate jumps in the asset prices.…”

Section: Risk Compensation and Asset Pricesmentioning

confidence: 99%

“…no costly learning. David (1997), Veronesi (1999), Hansen and Sargent (2010), and Ai (2010) consider learning models in which the agents learn about unobserved state variables. It is worth noting that learning considered in these models does not generate jumps in returns.…”

Section: Introductionmentioning

confidence: 99%

Learning and Asset-price Jumps

Bansal

Shaliastovich

2011

Rev. Financ. Stud.

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We develop a general equilibrium model in which income and dividends are smooth but asset prices contain large moves (jumps). These large price jumps are triggered by optimal decisions of investors to learn the unobserved state. We show that learning choice is determined by preference parameters and the conditional volatility of income process. An important model prediction is that income volatility predicts future jump periods, while income growth does not. Consistent with the model, large moves in returns in the data are predicted by consumption volatility but not by consumption growth. The model quantitatively captures these novel features of the data. 1 Learning and Asset-Price Jumps AbstractWe develop a general equilibrium model in which income and dividends are smooth, but asset prices are subject to large moves (jumps). A prominent feature of the model is that the optimal decision of investors to learn the unobserved state triggers large asset-price jumps. We show that the learning choice is critically determined by preference parameters and the conditional volatility of income process. An important prediction of the model is that the conditional volatility of income predicts future jump periods, while the level of income growth does not. We find that indeed in the data large moves in returns are predicted by consumption volatility, but not by the changes in consumption level. We show that the model can quantitatively capture these novel features of the data.

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“…Moreover, this posterior density has a dispersion that is constant and does not react to new information after transients have died out from startup of the filter. However, it is more realistic to have time-varying dispersion on the posterior density, suggesting periods of greater or lower confidence about the state of the dividend drift (David 1997). …”

Section: Introductionmentioning

confidence: 99%

Asset Pricing with Incomplete Information under Stable Shocks

2005

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We study a consumption based asset pricing model with incomplete information and α-stable shocks. Incomplete information leads to a non-Gaussian filtering problem. while they are not with a thin-tail distribution (Binomial distribution). This has the potential to generate time variation in the volatility of model-implied returns, without relying on discrete shifts in the drift rate of dividend growth rates. A test of the model using US consumption data indicates strong support in the sense that the implied returns display significant volatility persistence of a magnitude comparable to that in the data.

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Fluctuating Confidence in Stock Markets: Implications for Returns and Volatility

Cited by 229 publications

References 42 publications

Dynamic portfolio choice under ambiguity and regime switching mean returns

Dynamic portfolio choice under ambiguity and regime switching mean returns

Learning and Asset-price Jumps

Asset Pricing with Incomplete Information under Stable Shocks

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